Researchers at the University of Chicago Medicine Comprehensive Cancer Center aim to explore ways to slow or stop the growth of tumors that don’t respond to medication by utilizing artificial intelligence, machine learning and high-performance computing capabilities found at Argonne National Laboratory.
The Comprehensive Cancer Center will receive $6 million as part of an up to $15 million project that will leverage advanced AI/machine learning approaches to mine vast datasets and uncover patterns that can lead to the development of new treatments for drug-resistant cancer. Funding is provided by the Advanced Research Projects Agency for Health (ARPA-H), an agency within the U.S. Department of Health and Human Services established in 2022 to fast-track transformative biomedical research.
“The drug discovery process is long, inefficient and costly, with the majority of new drugs failing during clinical trials,” said Kunle Odunsi, who is the The AbbVie Foundation Distinguished Service Professor of Obstetrics and Gynecology at the University of Chicago; Comprehensive Cancer Center Director; and Dean for Oncology in the Biological Sciences Division. “Patients with cancer don’t have time to wait for new treatments, so there is a strong need to compress the drug discovery timeline and we aim to do that with novel synergistic approaches that take advantage of Argonne’s supercomputing capabilities and the strengths in chemistry and cancer biology at the University of Chicago.”
Cancer drug discovery is a complex and resource-intensive process, typically taking up to 15 years and more than $2 billion to take a drug from discovery of a target to FDA approval. In addition, while more than 4,500 human proteins are estimated to be druggable, less than 10% of them are currently targeted by approved drugs. For example, it is estimated that only 14% to 28% of patients with gynecologic cancers could be assigned to drugs based on the tumor molecular profile, meaning many patients do not have many effective treatment options.
The joint project, titled “Integrated AI and Experimental Approaches for Targeting Intrinsically Disordered ProtEins in Designing Anticancer Ligands” (IDEAL), will use cutting-edge technology and experimental approaches to narrow down the search to only the most promising compounds that can be translated into better treatments.
The UChicago Medicine team will be led by Odunsi as co-principal investigator and includes Christopher Weber, Savas Tay, and Bryan Dickinson. The Argonne team will be led by Dan Schabacker; Rick Stevens, Arvind Ramanathan, and Thomas Brettin, with support from Andrzej Joachimiak.
“With Argonne’s world-leading expertise in AI and the University of Chicago’s exceptional capabilities in cancer research, we are in a unique position to solve complex scientific challenges in cancer, one of the most pressing healthcare problems,” said co-investigator Thomas Brettin, strategic program manager in Argonne’s Computing, Environment and Life Sciences directorate.
Researchers will use Argonne’s unparalleled computing and experimental facilities: the Aurora exascale supercomputer at the Argonne Leadership Computing Facility and the ultrabright X-rays at Argonne’s Advanced Photon Source. These technologies will allow researchers to screen billions of possible molecules (including all drugs that are currently available) in a matter of a couple of hours and simulate thousands of complexes within days.
The IDEAL team will test this new model on targets known to be relevant in ovarian cancer, the deadliest of gynecologic cancers and one that is notoriously resistant to treatment. Although the pilot project will focus on ovarian cancer, the accelerated pipeline is intended to apply to any target for any cancer type.
“This project brings together stellar computational capabilities, the best structural biology resources, a world-class cancer center and some of the best scientists,” Odunsi said. “I believe this ‘dream team’ has the potential to revolutionize the cancer drug discovery timeline and change the paradigm for patients that currently have a poor prognosis and little hope for recovery.”
Adapted from an article first published by the UChicago Medicine Comprehensive Cancer Center.
—Prof. Jacqueline Stewart
